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Learning with Multiple Representations

Description du projet

Fondements théoriques d’une nouvelle branche de l’apprentissage automatique

Financé par le programme Actions Marie Skłodowska-Curie, le projet LEMUR entend développer les fondements théoriques et un premier ensemble d’algorithmes pour une nouvelle branche de l’apprentissage automatique (AA) appelée apprentissage avec représentations multiples (LMR). Ces algorithmes LMR permettront des représentations flexibles (simples et justes) avec diverses fonctions cibles (impact environnemental et social) afin de garantir qu’elles soient conformes à la Charte verte et aux critères de confiance de l’IA par conception. Le projet se concentrera sur l’apprentissage avec une supervision réduite, abordant l’un des principaux défauts des approches AA modernes. LEMUR fournira à 10 experts une formation éminemment interdisciplinaire et intersectorielle pour mettre en œuvre la troisième vague européenne d’IA et les suivantes.

Objectif

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g. as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g. suitable for explainability, fairness) with diverse target functions (e.g. incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e. their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.

Coordinateur

UNIVERSITAET PADERBORN
Contribution nette de l'UE
€ 260 539,20
Adresse
WARBURGER STRASSE 100
33098 Paderborn
Allemagne

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Région
Nordrhein-Westfalen Detmold Paderborn
Type d’activité
Higher or Secondary Education Establishments
Liens
Coût total
Aucune donnée

Participants (9)

Partenaires (10)